Evidence map›Paper›PMID 42575900›Full record

ArticleScientific data2026

A Dataset for Depth in Robotic Endoscopy with Dynamic Scenarios (DRENDS).

Gerardo Loza-Galindo, Mattia Magro, Benjamin Calmé, Junlei Hu, Emanuele Ruffaldi, Dominic Jones, Elena De Momi, Sharib Ali, Pietro Valdastri

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Gerardo Loza-GalindoSchool of Computer Science, University of Leeds, Leeds, LS2 9JT, UK. G.E.LozaGalindo@leeds.ac.uk.
Mattia MagroPolitecnico di Milano, Piazza Leonardo da Vinci, Milano, 32, 20133, Italy. mattia.magro@polimi.it.
Benjamin CalméSchool of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK.
Junlei HuSchool of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK.
Emanuele RuffaldiMedical Microinstruments, Inc., 5022 Gate Parkway, Jacksonville, 32256, FL, USA.
Dominic JonesSchool of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK.
Elena De MomiPolitecnico di Milano, Piazza Leonardo da Vinci, Milano, 32, 20133, Italy.
Sharib AliSchool of Computer Science, University of Leeds, Leeds, LS2 9JT, UK.
Pietro ValdastriSchool of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK.

Funding

Engineering and Physical Sciences Research Council UKRI914European Research Council 818045Horizon 2020 952118UK Research and Innovation EP/S024336/1
6 · The paper itself

Abstract

Depth perception in robotic minimally invasive surgery remains a critical challenge for many downstream tasks, demanding advanced depth estimation techniques and ground truth data for their validation. Current datasets lack data with ground-truth depth information in dynamic scenarios; therefore, we present DRENDS (Depth in Robotic Endoscopy with Dynamic Scenarios)

Indexed as

Depth PerceptionEndoscopyRobotic Surgical ProceduresAnimalsHumansLaparoscopyPhantoms, ImagingSwine

Identifiers

PMID42575900
PMCPMC13458812

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.